Social Influence Modeling on Smartphone Usage

Social Influence Modeling on Smartphone Usage
Masaji Katagiri1,2 and Minoru Etoh1,3
1 R&D Center, NTT DOCOMO, Inc. Yokosuka, Kanagawa, 239-8536 Japan [e-mail], [e-mail] Graduate School of Information Science and Technologies, Osaka University Suita, Osaka, 565-0871 Japan 3 Cybermedia Center, Osaka University Toyonaka, Osaka, 560-0043 Japan

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Abstract. This paper presents a probabilistic influence model for smartphone usage; it applies a latent group model to social influence. The probabilistic model is built on the assumption that a time series of students’ application downloads and activations can be represented by individual inter-personal influence factors which consist of latent groups. To verify that model with its assumption, about 160 university students voluntarily participated in a mobile application usage monitoring experiment. Analysis could identify latent user groups by observing predictive performance against reduced dimensions of factor matrices with NMF. Proper dimension reduction is shown to significantly improve predictive performance, which implies a reduction in the over-fitting phenomenon. With this improvement, the model outperforms conventional collaborative filtering models and popularity models in perplexity evaluation. The results validate the model and its assumption as well as its usefulness. Keywords: user influence, mobile application, recommendation, behavior prediction, latent structure analysis, matrix factorization, NMF.

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Introduction

Smartphones are becoming popular devices that can replace traditional cellular phones globally. One of their features is that various applications are available through the public network. In the case of Android phones, there is the “Android Market” site where people can find and download applications. Since the sheer number of applications makes it difficult for users to find the appropriate software, it is crucial to provide an appropriate search function and/or recommendation system

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